Drug-disease interactions (DDSIs) occur when drug effects, such as risks for rare severe adverse effects, are altered by a pre-existing disease. DDSIs affect up to 50% of older adults and have been associated with increased risk of death and use of health services. DDSIs are of particular concern in older adults because older people have more chronic illness and take more drugs. Although drug labels, treatment guidelines, and drug information services include warnings for thousands of drug-disease combinations, little evidence exists on their clinical relevance. DDSI guidance typically relies on case reports or similarities to related drugs, and thus commonly represents untested hypotheses rather than evidence from well-designed studies. As a result, physicians and their patients are often unable to distinguish between warnings for clinically relevant DDSIs that should be followed to avoid increased risk for adverse drug effects, and warnings for purely theoretical DDSIs that should be ignored in order to allow initiation of treatment with the otherwise indicated drug of choice. Better evidence on DDSIs is urgently needed to allow physicians to recommend evidence-based personalized therapy for their patients. The proposed study will generate actionable evidence for four carefully selected examples of hypothesized DDSIs of widely used drugs.
Using existing data resources on millions of patients from CPRD and US Medicare, the proposed study will use four carefully selected examples of highly prevalent drugs to demonstrate a new methodological framework for the systematic assessment of DDSIs from large observational datasets: metformin and renal impairment increasing risk of lactic acidosis (Aim 1), Z-drugs and osteoporosis increasing risk of hip fracture (Aim 2), systemic corticosteroids and peptic ulcer disease increasing risk of gastrointestinal bleeding (Aim 3), and allopurinol and renal impairment reducing risk of dialysis or kidney transplant (Aim 4). These examples were selected considering a number of explicit criteria including severity of the adverse outcome, disagreement about relevance in the literature and clinical practice, and availability of therapeutic alternatives that do not share the hypothesized DDSI. We included interactions across a spectrum of prevalence and expected effect sizes to evaluate the performance of the proposed approach in different situations and sought some effects very likely to be absent (Aim 1) or present (Aim 2) to show we can reproduce expected findings, and uncertain effects (Aims 3 and 4). The proposed study puts forward a novel framework that comprehensively classifies DDSIs according to their underlying biological mechanisms and represents the first systematic attempt to apply modern epidemiological and statistical methods to the examination of DDSIs. Separate implementation of all aims in two large healthcare databases provides built-in replication of results and facilitates assessment of the robustness of findings to differences in underlying data structure (claims vs. EHRs) and utilization patterns between different countries and health care systems (US vs. UK). Its results will begin a line of work that will ultimately enable physicians to practice evidence-based personalized medicine by providing reliable data on the effects of patient-specific comorbidities on the safety and effectiveness of their therapeutic regimens.
Aim 1 hospitalization for lactic acidosis, Aim 2 hospitalization for hip fracture, Aim 3 hospitalization for gastrointestinal bleed, Aim 4 dialysis/kidney transplant.
Tobias Gerhard - Chief Investigator - Rutgers, The State University of New Jersey
Haoqian Chen - Corresponding Applicant - Rutgers, The State University of New Jersey
- Collaborator -
Abner Nyandege - Collaborator - Rutgers, The State University of New Jersey
Amy Tyberg - Collaborator - Rutgers, The State University of New Jersey
Anupa Sharma - Collaborator - Rutgers, The State University of New Jersey
Brian Strom - Collaborator - Rutgers, The State University of New Jersey
Cecilia Huang - Collaborator - Rutgers, The State University of New Jersey
Isao Iwata - Collaborator - Rutgers, The State University of New Jersey
James Flory - Collaborator - Memorial Sloan Kettering Cancer Center
Mary Ritchey - Collaborator - Rutgers, The State University of New Jersey
Naomi Schlesinger - Collaborator - Rutgers, The State University of New Jersey
Reynold Panettieri - Collaborator - Rutgers, The State University of New Jersey
Richard Mann - Collaborator - Rutgers, The State University of New Jersey
Zhiqiang Tan - Collaborator - Rutgers, The State University of New Jersey
Avinash Gabbeta - Collaborator - Rutgers, The State University of New Jersey
Edward Nonnenmacher - Collaborator - Rutgers, The State University of New Jersey
HES Admitted Patient Care;Patient Level Index of Multiple Deprivation